Search Results (375)

Accurate estimation of erupted lava volumes is essential for understanding volcanic processes, interpreting eruptive cycles, and assessing volcanic hazards. Traditional methods based on Mid-Infrared (MIR) satellite imagery require clear-sky conditions during eruptions and are prone to sensor saturation, limiting data availability. Here, we present an alternative approach based on the post-eruptive Thermal InfraRed (TIR) signal, using the recently proposed VRP_TIR method to quantify radiative energy loss during lava flow cooling. We identify thermally anomalous pixels in VIIRS I5 scenes (11.45 µm, 375 m resolution) using the TIRVolcH algorithm, this allowing the detection of subtle thermal anomalies throughout the cooling phase, and retrieve lava flow area by fitting theoretical cooling curves to observed VRP_TIR time series. Collating a dataset of 191 mafic eruptions that occurred between 2010 and 2025 at (i) Etna and Stromboli (Italy); (ii) Piton de la Fournaise (France); (iii) Bárðarbunga, Fagradalsfjall, and Sundhnúkagígar (Iceland); (iv) Kīlauea and Mauna Loa (United States); (v) Wolf, Fernandina, and Sierra Negra (Ecuador); (vi) Nyamuragira and Nyiragongo (DRC); (vii) Fogo (Cape Verde); and (viii) La Palma (Spain), we derive a new power-law equation describing mafic lava flow thickening as a function of time across five orders of magnitude (from 0.02 Mm³ to 5.5 km³). Finally, from knowledge of areas and episode durations, we estimate erupted volumes. The method is validated against 68 eruptions with known volumes, yielding high agreement (R² = 0.947; ρ = 0.96; MAPE = 28.60%), a negligible bias (MPE = −0.85%), and uncertainties within ±50%. Application to the February-March 2025 Etna eruption further corroborates the robustness of our workflow, from which we estimate a bulk erupted volume of 4.23 ± 2.12 × 10⁶ m³, in close agreement with preliminary estimates from independent data. Beyond volume estimation, we show that VRP_TIR cooling curves follow a consistent decay pattern that aligns with established theoretical thermal models, indicating a stable conductive regime during the cooling stage. This scale-invariant pattern suggests that crustal insulation and heat transfer across a solidifying boundary govern the thermal evolution of cooling basaltic flows. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article

Assessing the impact of community-based conservation programs on wildlife biodiversity remains a significant challenge. This pilot study was designed to develop and demonstrate a scalable, spatially explicit workflow using thermal infrared (TIR) imagery and unmanned aerial vehicles (UAVs) for non-invasive biodiversity monitoring. Conducted in a 2-hectare grassland area in Chitwan, Nepal, the study applied TIR-based grid sampling and multi-species occupancy models with thin-plate splines to evaluate how species detection and richness might vary between (1) morning and evening UAV flights, and (2) the Chitwan National Park and Kumroj Community Forest. While the small sample area inherently limits ecological inference, the aim was to test and demonstrate data collection and modeling protocols that could be scaled to larger landscapes with sufficient replication, and not to produce generalizable ecological findings from a small dataset. The pilot study results revealed higher species detection during morning flights, which allowed us to refine our data collection. Additionally, models accounting for spatial autocorrelation using thin plate splines suggested that community-based conservation programs effectively balanced ecosystem service extraction with biodiversity conservation, maintaining richness levels comparable to the national park. Models without splines indicated significantly higher species richness within the national park. This study demonstrates the potential for spatially explicit methods for monitoring grassland mammals using TIR UAV as indicators of anthropogenic impacts and conservation effectiveness. Further data collection over larger spatial and temporal scales is essential to capture the occupancy more generally for species with larger home ranges, as well as any effects of rainfall, flooding, and seasonal variability on biodiversity in alluvial grasslands. Full article

Urban surface temperatures are increasing because of climate change and rapid urbanisation, contributing to the urban heat island (UHI) effect and significantly influencing local climates. Satellite-derived land surface temperature (LST) plays a vital role in analysing urban thermal patterns. However, current satellite thermal infrared (TIR) sensors have a low spatial resolution, making it difficult to accurately capture the complex thermal variations within urban areas. This limitation affects the assessments of UHI effects and hinders effective mitigation strategies. We proposed a hybrid model named “geospatial machine learning” (GeoML) to address these challenges, combining random forest and kriging downscaling techniques. This method utilises high spatial resolution data from Sentinel-2 to enhance the LST derived from Landsat 8/9 data. Tested in Perth, Australia, GeoML generated an enhanced LST with good agreement with ground-based measurements, with a Pearson’s correlation coefficient of 0.85, a root mean square error (RMSE) of 2.7 °C, and a mean absolute error (MAE) of less than 2.2 °C. Validation with LST derived from another TIR sensor also provided promising outputs. The results were compared with the high-resolution urban thermal sharpener (HUTS) downscaling methods, which GeoML outperformed, demonstrating its effectiveness as a valuable tool for urban thermal studies involving high-resolution LST data. Full article

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

The determination of precise and reliable interior (IO) and relative (RO) orientation parameters for thermal infrared (TIR) cameras is critical for their subsequent use in photogrammetric processes. Although 2D calibration boards have become the predominant approach for TIR geometric calibration, these targets are susceptible to projective coupling and often introduce error through manual construction methods, necessitating the development of 3D targets tailored to TIR geometric calibration. Therefore, this paper evaluates TIR geometric calibration results obtained from 2D board and 3D field calibration approaches, documenting the construction, observation, and calculation of IO and RO parameters. This includes a comparative analysis of values derived from three popular commercial software packages commonly used for geometric calibration: MathWorks’ MATLAB, Agisoft Metashape, and Photometrix’s Australis. Furthermore, to assess the validity of derived parameters, two InfraRed Thermography 3D-Data Fusion (IRT-3DDF) methods are developed to model historic building façades and medieval frescoes. The results demonstrate the success of the proposed 3D field calibration targets for the calculation of both IO and RO parameters tailored to photogrammetric data fusion. Additionally, a novel combined TIR-RGB bundle block adjustment approach demonstrates the success of applying ‘out-of-the-box’ deep-learning neural networks for multi-modal image matching and thermal modelling. Considerations for the development of TIR geometric calibration approaches and the evolution of proposed IRT-3DDF methods are provided for future work. Full article

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article

Semantic segmentation plays a critical role in understanding complex urban environments, particularly for autonomous driving applications. However, existing approaches face significant challenges under low-light and adverse weather conditions. To address these limitations, we propose CSANet (Context Spatial Awareness Network), a novel framework that effectively integrates RGB and thermal infrared (TIR) modalities. CSANet employs an efficient encoder to extract complementary local and global features, while a hierarchical fusion strategy is adopted to selectively integrate visual and semantic information. Notably, the Channel–Spatial Cross-Fusion Module (CSCFM) enhances local details by fusing multi-modal features, and the Multi-Head Fusion Module (MHFM) captures global dependencies and calibrates multi-modal information. Furthermore, the Spatial Coordinate Attention Mechanism (SCAM) improves object localization accuracy in complex urban scenes. Evaluations on benchmark datasets (MFNet and PST900) demonstrate that CSANet achieves state-of-the-art performance, significantly advancing RGB-T semantic segmentation. Full article

Land Surface Temperature (LST), as a core variable in the coupling of land–atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction method of the TIR-LST products from China’s Fengyun polar-orbiting satellite under dynamic cloud interference remains under exploration. This study focuses on the Heihe River Basin in western China, and addresses the issue of cloud coverage in relation to the Fengyun-3C (FY-3C) satellite TIR-LST. An innovative spatiotemporal reconstruction framework based on multi-source data collaboration was developed. Using a hybrid ensemble learning framework of random forest and ridge regression, environmental parameters such as vegetation index (NDVI), land cover type (LC), digital elevation model (DEM), and terrain slope were integrated. A downscaling and multi-factor collaborative representation model for land surface temperature was constructed, thereby integrating the passive microwave LST and thermal infrared VIRR-LST from the FY-3C satellite. This produced a seamless LST dataset with 1 km resolution for the period of 2017–2019, with temporal continuity across space. The validation results show that the reconstructed data significantly improves accuracy compared to the original VIRR-LST and demonstrates notable spatiotemporal consistency with MODIS LST at the daily scale (annual R² ≥ 0.88, RMSE < 2.3 K). This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking. Full article

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018–2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy. Full article

Accurate evapotranspiration (ET) monitoring is important for making scientific irrigation decisions. Unmanned aerial vehicle (UAV) remote sensing platforms allow for the flexible and efficient acquisition of field data, providing a valuable approach for large-scale ET monitoring. This study aims to enhance the accuracy and reliability of ET estimation in rice paddies through two synergistic approaches: (1) integrating the energy flux diurnal variations into the Two-Source Energy Balance (TSEB) model, which considers the canopy and soil temperature components separately, for physical estimation and (2) optimizing the flight altitudes and observation times for thermal infrared (TIR) data acquisition to enhance the data quality. The results indicated that the energy flux in rice paddies followed a single-peak diurnal pattern dominated by net radiation (Rn). The diurnal variation in the ratio of soil heat flux (G) to Rn could be well fitted by the cosine function with a max value and peak time (R² > 0.90). The optimal flight altitude and time (50 m and 11:00 am) for improved identification of temperature differentiation between treatments were further obtained through cross-comparison. These adaptations enabled the TSEB model to achieve a satisfactory accuracy in estimating energy flux compared to the single-source SEBAL model, with R² values of 0.8501 for Rn − G and 0.7503 for latent heat (LE), as well as reduced rRMSE values. In conclusion, this study presents a reliable method for paddy field scale ET estimation based on a calibrated TSEB model. Moreover, the integration of ground and UAV multimodal data highlights its potential for precise irrigation practices and sustainable water resource management. Full article

In this study, we investigated the influence of pre-fire tree mortality on fire behavior. Although other studies have focused on the environmental factors affecting wildfire, the influence of pre-fire tree mortality has not been explored in detail. We used high-spatial-resolution (1.6 m) airborne multispectral orthoimages to detect and map pre-fire dead trees in a portion of the San Bernardino Mountains, where the ‘Old Fire’ burned in 2003, and assessed whether spatial patterns of fire intensity and burn severity coincide with patterns of tree mortality. Dead trees were mapped through a hybrid deep learning classification and manual editing approach and facilitated with Google Earth Pro historical images. Apparent thermal infrared (TIR) brightness temperature captured during the Old Fire was derived from maximum digital number values from FireMapper airborne thermal infrared imagery (7 m) as a measure of fire intensity. Burn severity was analyzed using normalized burn ratio maps derived from pre- and post-fire Landsat 5 satellite imagery (30 m). Pre-fire dead trees were prevalent with 192 dead trees and 108 live trees per ha, with most dead trees clustered near the northwestern part of the study area east of Lake Arrowhead. The degree of spatial correspondence among dead tree density, fire intensity, and burn severity was analyzed using graphical and statistical analyses. The results revealed a significant but weak spatial association of dead trees with fire intensity (R² = 0.31) and burn severity (R² = 0.14). The findings revealed that areas impacted by pre-fire tree mortality were subject to higher fire intensity, followed by severe burn effects, though other biophysical factors also influenced these fire behavior variables. These results contradict a previous study that found no effect of tree mortality on the behavior of the Old Fire. Full article

Thermal heterogeneity of rivers is essential to support freshwater biodiversity. Salmon behaviorally thermoregulate by moving from patches of warm water to cold water. When implementing river restoration projects, it is essential to monitor changes in temperature and thermal heterogeneity through time to assess the impacts to a river’s thermal regime. Lightweight sensors that record both thermal infrared (TIR) and multispectral data carried via unoccupied aircraft systems (UASs) present an opportunity to monitor temperature variations at high spatial (<0.5 m) and temporal resolution, facilitating the detection of the small patches of varying temperatures salmon require. Here, we present methods to classify and filter visible wetted area, including a novel procedure to measure canopy cover, and extract and correct radiant surface water temperature to evaluate changes in the variability of stream temperature pre- and post-restoration followed by a high-intensity fire in a section of the river corridor of the South Fork McKenzie River, Oregon. We used a simple linear model to correct the TIR data by imaging a water bath where the temperature increased from 9.5 to 33.4 °C. The resulting model reduced the mean absolute error from 1.62 to 0.35 °C. We applied this correction to TIR-measured temperatures of wetted cells classified using NDWI imagery acquired in the field. We found warmer conditions (+2.6 °C) after restoration (p < 0.001) and median absolute deviation for pre-restoration (0.30) to be less than both that of post-restoration (0.85) and post-fire (0.79) orthomosaics. In addition, there was statistically significant evidence to support the hypothesis of shifts in temperature distributions pre- and post-restoration (KS test 2009 vs. 2019, p < 0.001, D = 0.99; KS test 2019 vs. 2021, p < 0.001, D = 0.10). Moreover, we used a Generalized Additive Model (GAM) that included spatial and environmental predictors (i.e., canopy cover calculated from multispectral NDVI and photogrammetrically derived digital elevation model) to model TIR temperature from a transect along the main river channel. This model explained 89% of the deviance, and the predictor variables showed statistical significance. Collectively, our study underscored the potential of a multispectral/TIR sensor to assess thermal heterogeneity in large and complex river systems. Full article

In the past, the possibility to retrieve the total column of water vapour (TCWV) from the thermal infrared (TIR) day and night measurements above water surfaces of the dual-view Along Track Scanning Radiometers (ATSR) has been demonstrated, and an algorithm, named Advanced InfrarRed Water Vapour Estimator (AIRWAVE), was developed and successfully applied to the measurements of the (A)ATSR instrument series. A similar instrument, the Sea and Land Surface Temperature Radiometer (SLSTR), is currently operating on board the Sentinel 3 satellite series. In this paper, we demonstrate that the AIRWAVE algorithm can be successfully applied to the SLSTR instrument to obtain reliable TCWV measurements. The steps performed for upgrading the algorithm are thoroughly described. The new AIRWAVE algorithm makes use of parameters computed offline with a state-of-the-art radiative transfer model using the most recent spectroscopic data and continuum model. For the parameters calculation, a new climatology capable of representing the average atmospheric and sea surface status during SLSTR measurements has been developed. The new algorithm, named AIRWAVE-SLSTR, has been implemented in both IDL and Python languages. In the frame of an EUMETSAT contract, AIRWAVE-SLSTR has been applied to a full year of SLSTR measurements (2021) and the retrieved TCWV have been validated with the help of both satellite- and ground-based measurements. The correlation of the retrieved TCWV with satellite MW measurements is 0.94 and the average bias is of the order of 0.66 kg/m${}^2$. When compared to ground-based measurements, the average correlation is 0.93 and the bias −0.48 kg/m${}^2$. The obtained accuracy is well within the requirements set for both numerical weather predictions (1–5 kg/m${}^2$) and for coastal altimetry applications (1.8–3 kg/m${}^2$). Therefore, the AIRWAVE-SLSTR algorithm can be safely applied to obtain a long time series of reliable TCWV above water surfaces. Full article